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Data science

By Reka Solymosi, Software Sustainability Institute Fellow.
From 4 – 5 April 2019, I attended the Women in Data Science Zurich 2019 conference as an invited speaker to talk about my research involving ‘new’ forms of data. In particular the use of crowdsourced data collection methods to gain insight into people’s perceptions and subjective evaluations of their environments.

By Alex Morley, Institute Fellow & Mozilla Fellow
It’s not a new concept. But when people talk to me about improving the scientific process it really resonates with me when they talk about feedback loops. This framework is broad enough to encompass most ways in which we can think about improving science, but also makes explicit what actions need to be taken, and where bottlenecks are likely to arise. Here are a few examples of how people have used these cycles to make/explain progress/problems in scientific processes.

By Danny Wong, NIAA-HSRC & UCL-DAHR. I’ve recently had the great fortune of publishing a paper which had significant interest from the general news media. It even managed to get picked up by the BBC, The Guardian and all the major newspapers in the UK! As per usual, I’ve shared the source code for the analysis publicly, this time electing to serve it up on GitHub as a repository. I have included the manuscript as an .Rmd file, and the wrangling data wrangling and modelling code as a chunk located at the start of the .Rmd file.

So… you’ve just started on an exciting new data science project, but you know nothing about the domain you’re working on. Besides briefly panicking, how do you get up to speed on the area you’re working on?

Steve Harris' article "Data Science for Docs" was recently published as the guest editorial in Bulletin, July 2017, the magazine for members of The Royal College of Anaesthetists, which reaches every anaesthetist in the UK (it's the largest hospital speciality).